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Please use this identifier to cite or link to this item: http://hdl.handle.net/10155/286

Issue Date: 1-Sep-2012
Title: Predicting mutation score using source code and test suite metrics
Authors: Jalbert, Kevin
Publisher : UOIT
Degree : Master of Science (MSc)
Department : Computer Science
Supervisor : Bradbury, Jeremy S.
Keywords: Machine learning
Mutation testing
Software metrics
Support vector machine
Test suite effectiveness
Abstract: Mutation testing has traditionally been used to evaluate the effectiveness of test suites and provide con dence in the testing process. Mutation testing involves the creation of many versions of a program each with a single syntactic fault. A test suite is evaluated against these program versions (i.e., mutants) in order to determine the percentage of mutants a test suite is able to identify (i.e., mutation score). A major drawback of mutation testing is that even a small program may yield thousands of mutants and can potentially make the process cost prohibitive. To improve the performance and reduce the cost of mutation testing, we proposed a machine learning approach to predict mutation score based on a combination of source code and test suite metrics. We conducted an empirical evaluation of our approach to evaluated its effectiveness using eight open source software systems.
Appears in Collections:Electronic Theses and Dissertations (Public)
Faculty of Science - Master Theses

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